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Machine Learning Algorithms for Risk Prediction of Severe Hand-Foot-Mouth Disease in Children.
Zhang, Bin; Wan, Xiang; Ouyang, Fu-Sheng; Dong, Yu-Hao; Luo, De-Hui; Liu, Jing; Liang, Long; Chen, Wen-Bo; Luo, Xiao-Ning; Mo, Xiao-Kai; Zhang, Lu; Huang, Wen-Hui; Pei, Shu-Fang; Guo, Bao-Liang; Liang, Chang-Hong; Lian, Zhou-Yang; Zhang, Shui-Xing.
Afiliación
  • Zhang B; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Wan X; Graduate College, Southern Medical University, Guangzhou, Guangdong, P.R. China.
  • Ouyang FS; Institute of Computational and Theoretical Study and Department of Computer Science, Hong Kong Baptist University, Hong Kong, P.R. China.
  • Dong YH; Department of Radiology, The First People's Hospital of Shunde, Foshan, Guangdong, P.R. China.
  • Luo DH; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Liu J; Department of Mathematics, Hong Kong Baptist University, Hong Kong, P.R. China.
  • Liang L; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Chen WB; Graduate College, Southern Medical University, Guangzhou, Guangdong, P.R. China.
  • Luo XN; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Mo XK; Graduate College, Southern Medical University, Guangzhou, Guangdong, P.R. China.
  • Zhang L; Department of Radiology, Huizhou Municipal Central Hospital, Huizhou, Guangdong, P.R. China.
  • Huang WH; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Pei SF; Graduate College, Southern Medical University, Guangzhou, Guangdong, P.R. China.
  • Guo BL; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Liang CH; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
  • Lian ZY; Graduate College, Southern Medical University, Guangzhou, Guangdong, P.R. China.
  • Zhang SX; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, P.R. China.
Sci Rep ; 7(1): 5368, 2017 07 14.
Article en En | MEDLINE | ID: mdl-28710409
The identification of indicators for severe HFMD is critical for early prevention and control of the disease. With this goal in mind, 185 severe and 345 mild HFMD cases were assessed. Patient demographics, clinical features, MRI findings, and laboratory test results were collected. Gradient boosting tree (GBT) was then used to determine the relative importance (RI) and interaction effects of the variables. Results indicated that elevated white blood cell (WBC) count > 15 × 109/L (RI: 49.47, p < 0.001) was the top predictor of severe HFMD, followed by spinal cord involvement (RI: 26.62, p < 0.001), spinal nerve roots involvement (RI: 10.34, p < 0.001), hyperglycemia (RI: 3.40, p < 0.001), and brain or spinal meninges involvement (RI: 2.45, p = 0.003). Interactions between elevated WBC count and hyperglycemia (H statistic: 0.231, 95% CI: 0-0.262, p = 0.031), between spinal cord involvement and duration of fever ≥3 days (H statistic: 0.291, 95% CI: 0.035-0.326, p = 0.035), and between brainstem involvement and body temperature (H statistic: 0.313, 95% CI: 0-0.273, p = 0.017) were observed. Therefore, GBT is capable to identify the predictors for severe HFMD and their interaction effects, outperforming conventional regression methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Aprendizaje Automático / Enfermedad de Boca, Mano y Pie Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Aprendizaje Automático / Enfermedad de Boca, Mano y Pie Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Child, preschool / Female / Humans / Infant / Male Idioma: En Revista: Sci Rep Año: 2017 Tipo del documento: Article Pais de publicación: Reino Unido